Deep Learning Based Formation Control for the Multi-Agent Coordination

In the paper, the authors took an attempt to briefly describe their experience in organizing work with data analysis using neural networks and machine

In this study, we developed a deep learning-based system for the MEDDOCAN task, a challenge special for de-identification of clinical text in Spanish. The system achieves

For hierarchical search tree, we first extract the category tree structure in the training dataset and use different classification algorithms to predict the top

this bottleneck is still valid. Deep learning is adapted to such problems where knowledge is implicit in the data. c) Deep neural networks are composed of a huge

This work focuses on reducing the impact of channel effects on identification performance and generalisation to changing conditions, something that has been little addressed in

This paper is organized as follows: The next section intro- duces the concept of knowledge mining. Section III describes the algorithms of induction rules mining. Then,

We tested Deep reinforcement learning for the optimal non-linear control of a chaotic system [1], using an actor-critic algorithm, the Deep Deterministic

This approach is aimed to strengthen the reliability of computational results by highlighting the invariant properties of the market setting under study with respect to an